The method we describe in this paper is a genealogical algorithm originally
proposed by [1]}, and subsequently
adapted to compute large deviation functions of time averaged observables
in numerical models [2]}, [3]}.
In [4]}, we have used this method to study European
heat waves, focusing on seasonal time scales. As we discussed in the
previous section, such times scales are out of the large deviation
asymptotics. Here we show instead how the algorithm can be extremely
useful to compute large deviation functions of climate observables,
overcoming the limitations of direct estimates. In the following we
refer to this algorithm as large deviation algorithm or Giardina-Kurchan-Lecomte-Tailleur
(GKLT) algorithm when used to compute large deviation functions, and
as the Del Moral-Garnier algorithm when used to simulate rare events
outside of the large deviation asymptotics.
